Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Introduction to WrenAI OSS
- Architectural overview of WrenAI
- Core open-source components and ecosystem
- Installation and initial configuration
Semantic Modeling with Wren AI
- Establishing semantic layers
- Creating reusable metrics and dimensions
- Best practices for maintaining consistency and manageability
Text to SQL in Practice
- Translating natural language into SQL queries
- Strategies for improving SQL generation precision
- Addressing common challenges and troubleshooting techniques
Prompt Tuning and Optimization
- Effective prompt engineering strategies
- Fine-tuning models for enterprise-scale datasets
- Achieving a balance between accuracy and system performance
Implementing Guardrails
- Mitigating risks from unsafe or expensive queries
- Setting up validation and approval workflows
- Considerations for governance and regulatory compliance
Integrating WrenAI into Data Workflows
- Embedding Wren AI into data pipelines
- Linking with BI and visualization platforms
- Managing multi-user and enterprise-level deployments
Advanced Use Cases and Extensions
- Developing custom plugins and API connections
- Augmenting WrenAI capabilities with additional ML models
- Scaling solutions for large volume datasets
Summary and Next Steps
Requirements
- Proficiency in SQL and database systems
- Hands-on experience with data modeling and semantic layers
- Working knowledge of machine learning or natural language processing principles
Target Audience
- Data engineers
- Analytics engineers
- ML engineers
